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All Journal International Journal of Electrical and Computer Engineering Register: Jurnal Ilmiah Teknologi Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Mantik Penusa JIPN (Journal of Informatics Pelita Nusantara) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer ComTech: Computer, Mathematics and Engineering Applications Jurnal Mantik Jurnal Sains dan Teknologi TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Jurnal Teknik Informatika C.I.T. Medicom Journal of Intelligent Decision Support System (IDSS) INFOKUM Journal of Computer Networks, Architecture and High Performance Computing International Journal of Basic and Applied Science Jurnal Media Informatika Journal Basic Science and Technology Jurnal Mandiri IT CEBONG Journal Journal of Computer Science and Research International Journal of Enterprise Modelling
Hengki Tamando Sihotang
Universitas Putra Abadi Langkat
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Journal : Jurnal Teknik Informatika C.I.T. Medicom

 1 
Sistem Pendukung Keputusan Penentuan Kesesuaian Lahan Tanaman Andaliman Dengan Metode Profile Matching Di Kecamatan Merdeka Kabupaten Karo Roma Sinta Simbolon; Hengki Tamando Sihotang
Jurnal Teknik Informatika C.I.T Medicom Vol 12 No 2 (2020): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol12.2020.31.pp64-71

Abstract

Andaliman is a commodity plant which has many benefits and high selling power. Andaliman plants are difficult to cultivate because of the difficulty of determining the suitability of land suitable for these plants and the absence of researchers who have conducted research on these plants. Therefore, a decision support system is needed that can assist in determining the suitability of andaliman plantations by using three criteria consisting of each sub-criteria, namely Soil Classification (which consists of soil Ph sub-criteria, soil type and humidity), Land conditions. (which consists of sub-criteria for altitude and light intensity), and climate (which consists of sub-criteria for air temperature and rainfall). A decision support system is a system which able to provide both problem-solving abilities and communication skills for semi-structured problems. This research system aims to design a system in determining the suitability of land for andaliman plants using the Profile Matching method, which is one of the methods in the DSS that uses the GAP weighting method. The programming language used in making the system is PHP with MySQL database. The results of the implementation of the system that have been made show that Jaranguda Village is the most suitable village for planting Andaliman plants.
Site Determination of Decision Support System Pelita Nusantara STMIK Campus Branch With the Electre Method Santiwati Sihotang; Hengki Tamando Sihotang
Jurnal Teknik Informatika C.I.T Medicom Vol 12 No 2 (2020): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol12.2020.33.pp56-63

Abstract

The problem that is often faced by the campus is ineffective in determining strategic locations. Failure to determine the location will be detrimental to the campus, resulting in the closure of the campus. To avoid this problem, analysis and appropriate methods are needed to assist in determining strategic campus locations. The Electre method is a decision-making method based on the Outranking concept that produces various alternatives to assist management in dealing with structured and unstructured problems, based on pairwise comparisons with alternatives that match the criteria and those that do not match the criteria will be eliminated. This method is needed as a Decision Support System (SPK) that can help the STMIK Pelita Nusantara campus, Medan in determining strategic campus locations. This study aims to apply the Electre method and build a Decision Support System for determining the location of the STMIK Pelita Nusantara branch campus. The method used in this study is the electre method to determine Population Density weighting, transportation access, location security, distance to other campuses, and high school / vocational school level education. Based on the analysis using the electro method, it is known that the strategic location of the campus branch can be used as an alternative, namely Medan Johor
Integration of stochastic and robust optimization techniques into DEA model for more accurate and reliable efficiency estimation Hengki Tamando Sihotang; Patricius Michaud Felix; Aisyah Alesha; Joan De Mathew
Jurnal Teknik Informatika C.I.T Medicom Vol 14 No 2 (2022): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol14.2022.229.pp1-4

Abstract

Efficiency assessment is vital to assessing decision-making units (DMUs) in numerous sectors. DEA is a prominent non-parametric efficiency assessment tool. Traditional DEA models assume deterministic inputs and outputs, ignoring real-world uncertainties and variability. To improve efficiency estimation, stochastic and robust optimization approaches can be integrated into DEA models. To improve efficiency estimation, we present a stochastic and robust optimization framework incorporating DEA. Probabilistic inputs and outputs allow stochastic optimization to account for uncertainty. The model can capture data variability and create stochastic DMU efficiency scores by adding probability distributions. For data uncertainties and outliers, the DEA model uses robust optimization. Robust optimization considers worst-case scenarios and minimizes extreme observations on efficiency estimation. This makes efficiency scores more resilient to data outliers and noise.  DEA models benefit from stochastic and resilient optimization. First, considering data uncertainties and fluctuations improves DMU efficiency representation. Second, eliminating outliers and extreme observations improves efficiency estimation. Third, efficiency scores help decision-makers make better, more informed choices. A case study in a specific industry shows the framework's efficacy. We compare classic and integrated stochastic-robust DEA model outcomes. The integrated model provides more accurate and dependable efficiency estimates, helping decision-makers understand DMU performance. DEA models with stochastic and resilient optimization increase efficiency estimation. By considering uncertainties and outliers, this paradigm helps decision-makers evaluate DMUs in many sectors more accurately and reliably.
Graph-based Exploration for Mining and Optimization of Yields (GEMOY Method) Sihotang, Hengki Tamando; Riandari, Fristi; Sihotang , Jonhariono
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 2 (2024): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.777.pp70-81

Abstract

This research explores the application of graph-based optimization techniques to enhance yield management and minimize transportation costs in industrial operations, particularly focusing on mining. By representing mining sites and processing plants as nodes and transportation routes as edges in a graph, we formulated an optimization problem aimed at maximizing yields while minimizing associated costs. Utilizing linear programming, we demonstrated significant cost savings, reducing transportation costs from 2100 units to 1700 units through optimized flow distribution. The study integrates elements of graph theory, optimization algorithms, and machine learning, providing a robust framework for efficient resource allocation and operational planning. The numerical example underscores the practical applicability of these techniques, paving the way for further research and refinement to accommodate additional constraints and dynamic changes in resource availability. This research highlights the potential of graph-based methods to achieve substantial economic and operational improvements across various industrial contexts.
Optimizing supply chain efficiency: Advanced decision support systems for enhanced performance Judijanto, Loso; Lemos, Sgarbossa Carlo; Sihotang , Jonhariono; Sihotang , Hengki Tamando
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 3 (2024): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.857.pp185-198

Abstract

This research investigates the optimization of supply chain efficiency through the application of advanced Decision Support Systems (DSS), focusing on minimizing operational costs while maintaining high service levels. The main objective is to explore how DSS, integrated with real-time data, artificial intelligence (AI), and machine learning (ML), can enhance decision-making processes across production, inventory management, and transportation. The research employs a multi-objective optimization model, developed to minimize production, inventory, transportation, and shortage costs, while dynamically adjusting decisions based on real-time demand and supply data. A numerical example is used to test the model’s effectiveness, revealing significant cost reductions in production and transportation but highlighting challenges in maintaining consistent service levels. The results indicate that DSS can substantially improve supply chain efficiency by enabling data-driven decisions in real time, though its adoption remains limited by technical and scalability challenges, particularly for small-to-medium enterprises (SMEs). This study contributes to the growing body of knowledge on supply chain optimization, offering practical insights into DSS implementation and its potential impact on operational performance. The conclusions suggest that future research should focus on developing more sophisticated DSS models capable of handling uncertainty, sustainability, and resilience, as well as enhancing scalability to make DSS more accessible to a broader range of businesses.
Co-Authors A, Galih Prakoso Rizky Achiriani, Tri Wahyuningtiyas Agustina Simangunsong Aisyah Alesha Aisyah Alesha Alrasyid, Wildan Anthoni Anggrawan Anthony Anggrawan Bambang Saras Yulistiawan Bosker Sinaga Budi Arif Dermawan Calvin Berkat Iman Hulu Chandra, Suherman Dadang Pyanto Delano, Aldrich Desi Vinsensia Dini Anggraini Dwiki Rivaldo Naidu Efendi, Syahril Elpridawati Purba Endang Mistaorina Laia Erwin Panggabean Fadiel Rahmad Hidayat Firmansyah Firmansyah Fransisco alexander Simbolon Fristi Riandari Guntur Syahputra Harapan Lumbantoruan Harapan Lumbantoruan Harpingka Fitria Br. Sibarani Harpingka Fitriai Br. Sibaran Hasugian , Paska Marto Herlina Zebua Herman Mawengkang Herman Mawengkang Husain Husain Hutahaean, Harvei Desmon Jacob, Halburt Jane Irma Sari Jelita Sari Simanungkalit Jijon Raphita Sagala Joan De Mathew Jonhariono Sihotang Jonhariono Sihotang Judijanto, Loso Kouvelis Geovany Ortizan Laia, Endang Mistaorina Lemos, Sgarbossa Carlo Lise Pujiastuti Maria Santauli Siboro Martinus Ndruru Melda Agustina Nababan Michaud, Patrisius Mochamad Wahyudi Muhammad Rafli Muhammad Zarlis Mulianingtyas, RR Octanty Murni Marbun Normi Verawati Marbun Panjaitan, Firta Sari Patricius Michaud Felix Patrisia Teresa Marsoit Pilisman Buulolo R. Mahdalena Simanjorang Rasenda, Rasenda Riandari, Fristi Rifka Widyastuti, Rifka Ririn Pebrina Br. Marpaung Rizky A, Galih Prakoso Rizky, Galih Prakoso Rohit Gautama Roma Sinta Simbolon Rosulastri Purba Santiwati Sihotang Santoso, Heroe Sethu Ramen Sihotang , Jonhariono Sihotang, Jonhariono Sim, Lee Choi Simbolon, Agata Putri Handayani Simbolon, Roma Sinta Siringoringo , Rimmar Siskawati Amri Sitio, Arjon Samuel Song , Jiang Lou Sri Devi Sulindawaty, Sulindawaty Tarisa Tarigan Teresa, Patrys Vina Winda Sari Vinsensia, Desi